AMMI and GGE biplot analysis of genotype by environment interaction and yield stability in early maturing cowpea [Vigna unguiculata (L) Walp] landraces in Ethiopia

Abstract Cowpea is one of the most important grain legumes for human consumption and animal feeding. Despite this importance, its production is hampered by biotic and abiotic constraints. Genotype by environment interaction study was performed to identify the most stable cowpea genotype(s) and the desirable environment(s) for cowpea research in Ethiopia. Twenty‐four cowpea landraces and one standard check were evaluated for grain yield and yield‐related traits at six locations (Sekota, Kobo, Sirinka, Melkassa, Mieso, and Babile) using 5 × 5 triple lattice during 2019. Combined analysis of variance showed that grain yield was significantly affected by environments, genotypes, and GE interactions. AMMI analysis revealed the contribution of environment, genotype, and GEI for 29.79%, 15.6%, and 42.06% of variation on grain yield. The first two principal components explained 57.97% of the total GEI variance. AMMI model selected G24 as 1st and 2nd best genotype at five environments. The polygon view of the GGE biplot identified three mega‐environments (ME1, ME2, and ME3) with winning genotypes: G24, G3, and G16, respectively. The highest productive (2528.8 kg ha−1) environment, miesso has been identified as the most; discriminating and representative testing environment whereas the lowest productive (1676.1 kg ha−1) Sirinka was the least discriminating and representative. The highest yielder G24 (2632 kg ha−1) was identified as the “ideal” and the most stable genotype followed by G16 (2290 kg ha−1) while the least stable and low yielder was G11. Therefore, genotypes G24 and G16 were recommended for verification and commercial production in most cowpea growing areas of Ethiopia.

multiplicative interaction (AMMI) and GGE biplot analysis are the best approaches to quantify genotype by environment interaction, classification of mega-environments and characterization of testing environments, and simultaneous selection of genotypes based on stability and mean yield (Gauch & Zobel, 1996;.
Even though there are many opportunities for breeders to develop cowpea varieties possessing different agronomic characteristics and tolerance to a wide range of biotic and abiotic factors, the progress of cowpea breeding in Ethiopia is very slow, either in exploiting the available genetic variability in the country or from the introduction of improved varieties. Moreover, information on the effect of genotype, environment, and their interaction, and the performance stability of cowpea landraces is scanty. Therefore, this study was initiated to estimate the effects of genotype, environment, and their interaction on grain yield and related traits and to assess the stability of cowpea landrace for yield across environments.

| Description of the study area
The experiment was conducted in six cowpea growing environments during the 2019 main cropping season. These six locations representing different agro-ecologies of cowpea growing areas in Ethiopia were selected based on representativeness for cowpea producing environments. Descriptions of these six areas are presented in Table 1 below. NLLP_CPC_07_48B, NLLP_CPC_07-03, and NLLP_CPC 07-55) collected from different regions were evaluated along with one standard check (released variety: Kanketi). The experiment was conducted using 5 × 5 triple lattice design at each location and each genotype was assigned randomly. The seeds were planted on 4 m × 2.4 m (9.6 m 2 ) plots having four rows, with inter and Intra row spacing of 60 and 20 cm, respectively. The net harvest area was 4.8 m 2 per plot, the central two rows. The spacing between plots and blocks was 1 and 1.5 m, respectively. Sowing was carried out in July and all agronomic management practices were done equally and properly as per local recommendations. AMMI model with first and second multiplicative terms is adequate for cross-validation of the yield variation explained by GEI (Gauch et al., 2008).

| Data collection and data analysis
Among the testing environments, grain yields were highest at Miesso as compared to the other five environments with a mean grain yield of 2528.8 kg ha −1 followed by Melkassa (2487.1 kg ha −1 ) and Kobo (1986.6 kg ha −1 ) ( Table 5). The lowest grain yield was obtained at Sirinka with a mean yield of 1676.1 kg ha −1 . The superior performance of genotypes at Miesso and Melkassa can be attributed to the uniform distribution of rainfall throughout the cropping season. The tested genotypes showed inconsistent yield advantage across environments. The mean grain yield of genotypes over environments in Table 5 indicated that G24 (2632 kg ha −1 ) and G16 (2290 kg ha −1 ) were the highest yielding genotypes whereas genotype G11 was the lowest yielder (1535 kg ha −1 ).
The sign of the IPCA scores indicates the pattern of interaction of the genotypes across the environments and vice versa. Genotypes and environments with a similar sign of their IPCA sores interact positively for that trait. Sekota and Sirinka were different from the other locations in both the interaction and for the main effects (Table 5) Accordingly, genotypes: G16, G5, G4, G2, G15, and G24 showed relatively smaller IPCA1 scores thus are considered to be stable and had wider adaptation while, G3, G8, G19, and G1showed higher IPCA1 scores, respectively (Table 5). Similar to genotypes, environments with higher IPCA scores discriminate among genotypes more than environments with lesser scores. Thus, Sirinka was the most discriminating environment for the genotypes as indicated by the longest distance between its marker and the origin, followed by Kobo. However, due to their high IPCA scores, genotypic variability at this environment (Sirinka) may not exactly reflect the average performance across environments.

| Four best genotypes selections of AMMI model
The highest yielding genotype (G24) was among the four best genotypes selected by the AMMI model and had selected as 1st best genotype at four environments and as 2nd best genotype at one environment (Table 6). This genotype was selected both at favorable environments (environmental mean yield greater than the grand mean) and unfavorable (environmental mean yield less than the grand mean), suggesting that it is desirable for cultivation in both environments.
Similarly, the second-highest yielder genotype (G16) was selected at two unfavorable environments and one favorable environment as 1st and 2nd best genotype whereas the third-highest yielding genotype (G2) was selected at one unfavorable environment (sekota) as 3rd best genotype. According to AMMI's best four selections, Genotypes G24, G18, G4, and G25 were desirable for both favorable and unfavorable environments but G18 grain yield was lower than the grand mean.
G9 and G13 were more desirable in favorable environments whereas G2, G1, G22, G8, G23, and G3 were desirable in unfavorable environments. The selection of these genotypes in respective environments by the AMMI model is an indication of the best adaptation of the genotypes at those particular environments.

Source of variation df
Sum of square

| GGE biplot for evaluation of genotypes and environments
The residual mean square from the AMMI model for grain yield was highly significant (Table 4) which suggested that the importance of constructing GGE biplot to visualize "Which-won-where" Patterns of genotypes and environments and the discriminating ability and representativeness of the environments. In the present study, the GGE biplot graphic analysis of 25 cowpea genotypes revealed that the first two principal components explained 57.97% of the total GEI variance (Figure 1).

TA B L E 6
The first four best cowpea landraces selected for mean yield by the AMMI model per environment A polygon view of the GGE biplot was formed by connecting the vertex genotypes with straight lines and the rest of the genotypes were placed within the polygon. G3, G24, G16, G19, and G11 were vertex genotypes and they are best in the environment lying within their respective sector in the polygon view of the GGE biplot (Mehari et al., 2015); thus these genotypes performed either the best or the poorest in one or more locations since they had the longest distance from the origin of the biplot. According to Yan and Tinke (2006) and Gauch et al. (2008), genotypes within the polygon and nearer to the origin of the axes have wider adaptation and less response for environmental variation. Yan and Rajcan (2002) reported that responsive genotypes were those having either best or the poorest performance in one or all environments. G24 and G16 were identified as the highest yielding genotypes whereas G19 were considered as the lowest yielding genotype among vertex genotypes. In addition, no environment fell inside the sectors of the vertex genotypes G11 and G19, which indicated that those vertex genotypes were not the best in any of the test environments.
Another interesting feature of the GGE biplot is the identification of mega-environments as well as their winning genotypes.
The present investigation suggested the existence of three cowpea and Melkassa (E4) fell inside mega-environment three (ME3). The vertex genotypes in each sector are the best genotype in environments whose markers fall into the respective sector. Environments within the same sector share the same winning genotypes, and environments in different sectors have different winning genotypes.
Accordingly, Genotypes G24, G3, and G16 are suggested as the winner and highest yielding genotypes in mega-environment one, two, and three, respectively.  reported that the polygon view of GGE biplot is the best way for the identification of winning genotypes with visualizing the interaction patterns between genotypes and environments.
An ideal genotype is defined as a genotype with the greatest PC1 score (mean performance) and with zero GEI, as represented by an arrow pointing to it (Figure 2). Even though such type of genotype may not exist in reality, it can be used as a reference for the evaluation of genotypes Yan & Tinke, 2006). If a genotype is located closer to the ideal genotype, it becomes more desirable than other genotypes which are located far away from the ideal genotype. Therefore, concentric circles were drawn around the central circle which contains the ideal genotype to visualize the distance between each genotype and the ideal genotype. From the present investigation, G24 was the "ideal" genotype, with the highest mean grain yield and thus considered as the most stable across variable environments. Simultaneously, G23, G4, and G20 genotypes were located closer to the ideal genotype and were considered as desirable genotypes.

| Discriminating ability and representativeness of environments
According to , the discriminating ability and representativeness view of the GGE biplot is the important measure of test environments, which provide valuable and unbiased information about the tested genotypes. Yan and Tinke (2006) also reported that Environments with longer vectors had the more discriminating ability of the genotypes whereas environments with very short vectors had little or no information on the genotype difference. From this study, the test environments Sirinka (E3) and Miesso (E5) were identified as the most discriminating environments which provided much information about differences among genotypes, while Sekota (E1), Melkassa (E4), and Babile (E6) provided little information about the genotype differences ( Figure 3).
Another equally important measure of a test environment is its representativeness of the target environments. If a test environment is not representative of the target environments, it is not only useless but also misleading since it may provide biased information about the tested genotypes (Yan & Kang, 2003 Thus, based on the size of the angle between the vector of an environment and the abscissa of the average environment coordination (AEC) axis, it is possible to measure the representativeness of a testing environment. That is, a testing environment that makes an acute angle with AEC axis has a positive correlation with other testing environments and it is considered as a representative of the other testing environments, whereas the testing environment that makes an obtuse angle with AEC axis has a negative correlation with other testing environments and least representative (Yan et al., 2007;Yan & Tinke, 2006). From this study, Miesso was identified as the most representative testing environment, which was able to provide unbiased information about the performance of the tested genotypes, whereas Sirinka was identified as the least representative testing environment ( Figure 3).
The ideal test environment is an environment that has more power to discriminate genotypes in terms of the genotypic main effect as well as being able to represent the overall environments. It is used for selecting generally adaptable genotypes but obtaining such type of environment is very difficult in real conditions. In such condition, environments that fell near to a small circle located in the center of concentric circles and an arrow pointing on it (ideal environment) is identified as the best desirable testing environments (Yan & Rajcan, 2002). Among the testing environments used in this study, Miesso (E5) was identified as an ideal environment in terms of being the most representative of the overall environments and powerful to discriminate genotypes (Figure 4) This genotype was selected both at favorable and unfavorable environments, suggesting that it is desirable for cultivation in both environments.
The polygon view of the GGE biplot identified three megaenvironments (ME1, ME2, and ME3) with winning genotypes: G24, G3, and G16 respectively. The highest productive (2528.8 kg ha −1 ) environment, miesso has been identified as the most; discriminating and representative testing environment whereas the lowest productive (1676.1 kg ha −1 ) Sirinka was the least discriminating and representative. The highest yielder genotype G24 (2632 kg ha −1 ) was identified as the "ideal" and the most stable genotype followed by G2 (2276 kg ha −1 ), G4 (2250 kg ha −1 ), G20 (2213 kg ha −1 ) and G16 (2290 kg ha −1 ) were most stable genotypes with no statistical significant difference in mean grain yield, however, only the first three genotypes exceeded the standard check variety kanketi in grain yield. Therefore, genotypes G24 and G16 were recommended for verification and commercial production in most cowpea growing areas of Ethiopia.

CO N FLI C T O F I NTE R E S T
The authors declared no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are openly available in Yirga at https://www.resea rchga te.net/profi le/Yirga_Wasihun.